“Scaling” the Challenges in Subsurface Simulations

Integrating biogeochemical models across multiple time and spatial scales

Important biogeochemical
processes (e.g., microbial respiration) are best
understood and defined at very small scales, typically ranging from molecular
to cellular to small batch and column experimental scales (with time scales of
minutes to days). However, the scales at which we are interested in predicting
phenomena (e.g., fate and transport of contaminants in aquifers) are very large,
typically ranging from meters to kilometers (with time scales of months to years
or even centuries). This problem is aggravated by the variability of natural
subsurface properties that exist across the broad spectrum of spatial and
temporal scales. Recent experimental research has revealed important details
about the physical, chemical, and biological mechanisms involved in these
processes at a variety of scales ranging from molecular to laboratory scales.
However, it has not been practical or possible to translate detailed knowledge
at small scales into reliable predictions of field-scale phenomena important
for environmental management applications.

From a computational standpoint, this problem is manifested in the fact that
numerical grids cannot simultaneously cover large (e.g., field-scale) domains
and achieve extremely high (e.g., pore-scale) resolution. In simple terms, we
cannot computationally solve problems at the scale of tens of meters (let alone
kilometers) while explicitly resolving pore and grain structures, even if we had
characterization data to populate such a model. A large assortment of numerical
simulation tools have been developed, each with its own characteristic scale.
However, integration of these tools into a coherent multi-scale modeling framework
has not been seriously attempted in the subsurface modeling field (although other
disciplines have made significant advances in this area). Furthermore, most
applications of the available simulation tools do not utilize high-performance
computational facilities because of the investment required to parallelize and
optimize performance of codes, the complexities of data management and visualization,
and other barriers (perceived or real) between the computational and domain sciences.

This project will customize and apply existing multiscale hybrid modeling
methodologies to subsurface science, in the context of high-performance
computing, to advance both scientific understanding and predictive capability
that is applicable to field-scale problems.

Biogeochemical reactions occurring at very small scales
often control the subsurface mobility of contaminant metals and radionuclides
but are poorly simulated at the field scale. Hybrid modeling methodologies adapted
for use on high performance computers that address issues of scale will be developed
and evaluated against benchmark data to provide more accurate descriptions of
subsurface processes controlling the mobility of contaminants at DOE facilities.